Hydrochlorination of acetylene using expanded multilayered vermiculite(EML-VMT)-supported catalysts

Catalyst supports have very important effects on catalyst performance.A novel expanded multilayered vermiculite（EML-VMT） is successfully used as the catalyst support for the acetylene hydrochlorination.By mixing carbon on the surface of EML-VMT[i.e.,EML-VMT-C）,the HgCl2/EML-VMT-C achieved a high acetylene conversion of 97.3%,a vinyl chloride selectivity of 100%and a turn over frequency（TOF） value of 8.83 × 10^-3s^-1 at a temperature of 140 C,an acetylene gas hourly space velocity（GHSV） of 108 h^-1,and a feed volume ratio V（HC1）/V（C2H2） of 1.15.Moreover,the HgCl2/EML-VMT-C shows good stability.The EML-VMT also shows potential in the preparation of other EML-VMT-supported catalysts.

Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR

Near infrared spectroscopy （NIR） is now probably the most popular process analytical technology （PAT） for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm （GA） and support vector machine （SVM）. GA is used for wavelength selection and SVM for mode building. The new GA-SVM approach is shown to outperform other methods including GA-PLS （partial least squares） and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of L-glutamic acid.

An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms

In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.